In Section , an efficient DRL algorithm has been proposed to solve the sub-band assignment and power allocation problem in a stationary V2X communications system. The algorithm is designed based on the assumption that the communications environment and QoS requirements remain the same during the training and testing stages. However, this assumption is impractical in real-time V2X communications. Existing algorithms lack the adaptability for different communications scenarios since there will be mismatch if the testing environment follows a different distribution from the training environment. To address this challenge, we propose a meta reinforcement learning algorithm, which can provide a good reinforcement learning model that has the generalization ability to new environments and new tasks. The meta reinforcement learning algorithm is designed by incorporating the idea of the MAML algorithm [1]} into the proposed joint RL framework. The MAML-based reinforcement learning proposed in [1]} cannot be directly used to solve our problem since it focuses on solving the on-policy MDP. The off-policy MDP is considered in our resource allocation problem. In the following, we describe the proposed meta reinforcement learning algorithm for our problem based on the idea of the MAML framework.
